A Multi-Omics Longitudinal Aging Dataset in Primary Human Fibroblasts with Mitochondrial Perturbations

Abstract

Aging is a process of progressive change. In order to develop biological models of aging, longitudinal datasets with high temporal resolution are needed. Here we report a multi-omic longitudinal dataset for cultured primary human fibroblasts measured across their replicative lifespans. Based on the accelerated nature of epigenetic aging in vitro, these longitudinal data are equivalent to ~40 years of follow-up sampling at a frequency of every ~3 years. Fibroblasts were sourced from both healthy donors (n=6) and individuals with lifespan-shortening mitochondrial disease (n=3). The dataset includes cytological (cell size, morphology), bioenergetic (energy expenditure, derived ATP synthesis rates), epigenetic (DNA methylation), gene expression (RNA sequencing), secreted proteins, mitochondrial DNA (mtDNA) copy number and mutations, cell-free DNA, telomere length, and whole-genome sequencing data. This dataset enables the bridging of mechanistic processes of aging as outlined by the “hallmarks of aging”, with the descriptive characterization of aging provided by epigenetic clocks and other metrics. Here we focus on bridging the gap for the hallmark mitochondrial metabolism. Our dataset includes measurement of healthy cells replicating through senescence without intervention, as well as cells subjected to over a dozen experimental manipulations targeting oxidative phosphorylation (OxPhos), glycolysis, and glucocorticoid signaling, among others. These experiments provide opportunities to test how cellular energetics affect the biology of cellular aging. All data are publicly available at our webtool: https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study/

Cellular Allostatic Load is linked to Increased Energy Expenditure and Accelerated Biological Aging

“…Finally, our analyses of the RNA sequencing and DNA methylation omics datasets are only partial, and more complete analyses of these data beyond the scope of the present manuscript could yield further insights into the global, longitudinal recalibrations and mechanisms underlying glucocorticoid-driven cellular allostatic load and the resulting allostatic overload. To examine these and related questions, we make available as a community resource the multi-omics dataset of chronic Dex (as well as other treatments, see Data Availability Statement ) 26 .…”

“…Primary human fibroblasts of 3 healthy donors were obtained from certified distributors and described in detail in 26 . The characteristics of the three cell lines are summarized in Table 1.…”

“…Chronological age can also be predicted with high accuracy through DNA methylation-based algorithms, known as epigenetic clocks [67][68][69][70][71] . Here, we generated a longitudinal DNA methylation dataset using the EPIC array 26 , and deployed an approach that increases DNAmAge accuracy using a principal components-adjustment of the classic epigenetic clocks 72 . Applied across the cellular lifespan, we can longitudinally quantify the rate of epigenetic aging relative to population doublings.…”

“…All data presented in this manuscript are available for download at our Cellular Lifespan Study shiny app: https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study, with additional data described in 26 . Source data and fibroblast images are available at https://doi.org/10.6084/m9.figshare.18441998.v2 and https://doi.org/10.6084/m9.figshare.18444731.v1, respectively.…”

“…Here we use a cellular system shown to recapitulate human epigenetic aging signatures in vitro 25 and enable to longitudinally examine several key hallmarks of human aging, including mitochondrial bioenergetics, secreted cytokines, epigenetic clocks, and markers of replicative senescence such as telomere length and the Hayflick limit 26 . To examine the bioenergetic and cellular manifestations of allostatic load across the lifespan, we apply chronic GC stimulation to three cell lines derived from three healthy donors, and deploy a longitudinal, high-frequency, repeated-measures approach.…”